Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output
Parameters of a process-based forest growth simulator are difficult or impossible to obtain from field observations. Reliable estimates can be obtained using calibration against observations of output and state variables. In this study, we present a Bayesian framework to calibrate the widely used pr...
| Main Authors: | , , , |
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| Format: | Article |
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European Geosciences Union
2018
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| Online Access: | https://eprints.nottingham.ac.uk/53022/ |
| _version_ | 1848798861176340480 |
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| author | Raj, Rahul Tol, Christiaan van der Hamm, Nicholas A.S Stein, Alfred |
| author_facet | Raj, Rahul Tol, Christiaan van der Hamm, Nicholas A.S Stein, Alfred |
| author_sort | Raj, Rahul |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Parameters of a process-based forest growth simulator are difficult or impossible to obtain from field observations. Reliable estimates can be obtained using calibration against observations of output and state variables. In this study, we present a Bayesian framework to calibrate the widely used process-based simulator Biome-BGC against estimates of gross primary production (GPP) data. We used GPP partitioned from flux tower measurements of a net ecosystem exchange over a 55-year-old Douglas fir stand as an example. The uncertainties of both the Biome-BGC parameters and the simulated GPP values were estimated. The calibrated parameters leaf and fine root turnover (LFRT), ratio of fine root carbon to leaf carbon (FRC : LC), ratio of carbon to nitrogen in leaf (C : Nleaf), canopy water interception coefficient (Wint), fraction of leaf nitrogen in RuBisCO (FLNR), and effective soil rooting depth (SD) characterize the photosynthesis and carbon and nitrogen allocation in the forest. The calibration improved the root mean square error and enhanced Nash–Sutcliffe efficiency between simulated and flux tower daily GPP compared to the uncalibrated Biome-BGC. Nevertheless, the seasonal cycle for flux tower GPP was not reproduced exactly and some overestimation in spring and underestimation in summer remained after calibration. We hypothesized that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. We investigated this by calibrating the Biome-BGC to each month's flux tower GPP separately. As expected, the simulated GPP improved, but the calibrated parameter values suggested that the seasonal cycle of state variables in the simulator could be improved. It was concluded that the Bayesian framework for calibration can reveal features of the modelled physical processes and identify aspects of the process simulator that are too rigid. |
| first_indexed | 2025-11-14T20:26:30Z |
| format | Article |
| id | nottingham-53022 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:26:30Z |
| publishDate | 2018 |
| publisher | European Geosciences Union |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-530222020-05-04T19:26:00Z https://eprints.nottingham.ac.uk/53022/ Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output Raj, Rahul Tol, Christiaan van der Hamm, Nicholas A.S Stein, Alfred Parameters of a process-based forest growth simulator are difficult or impossible to obtain from field observations. Reliable estimates can be obtained using calibration against observations of output and state variables. In this study, we present a Bayesian framework to calibrate the widely used process-based simulator Biome-BGC against estimates of gross primary production (GPP) data. We used GPP partitioned from flux tower measurements of a net ecosystem exchange over a 55-year-old Douglas fir stand as an example. The uncertainties of both the Biome-BGC parameters and the simulated GPP values were estimated. The calibrated parameters leaf and fine root turnover (LFRT), ratio of fine root carbon to leaf carbon (FRC : LC), ratio of carbon to nitrogen in leaf (C : Nleaf), canopy water interception coefficient (Wint), fraction of leaf nitrogen in RuBisCO (FLNR), and effective soil rooting depth (SD) characterize the photosynthesis and carbon and nitrogen allocation in the forest. The calibration improved the root mean square error and enhanced Nash–Sutcliffe efficiency between simulated and flux tower daily GPP compared to the uncalibrated Biome-BGC. Nevertheless, the seasonal cycle for flux tower GPP was not reproduced exactly and some overestimation in spring and underestimation in summer remained after calibration. We hypothesized that the phenology exhibited a seasonal cycle that was not accurately reproduced by the simulator. We investigated this by calibrating the Biome-BGC to each month's flux tower GPP separately. As expected, the simulated GPP improved, but the calibrated parameter values suggested that the seasonal cycle of state variables in the simulator could be improved. It was concluded that the Bayesian framework for calibration can reveal features of the modelled physical processes and identify aspects of the process simulator that are too rigid. European Geosciences Union 2018-01-09 Article PeerReviewed Raj, Rahul, Tol, Christiaan van der, Hamm, Nicholas A.S and Stein, Alfred (2018) Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output. Geoscientific Model Development, 11 (1). pp. 83-101. ISSN 1991-9603 http://dx.doi.org/10.5194/gmd-11-83-2018 doi:10.5194/gmd-11-83-2018 doi:10.5194/gmd-11-83-2018 |
| spellingShingle | Raj, Rahul Tol, Christiaan van der Hamm, Nicholas A.S Stein, Alfred Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output |
| title | Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output |
| title_full | Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output |
| title_fullStr | Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output |
| title_full_unstemmed | Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output |
| title_short | Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output |
| title_sort | bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output |
| url | https://eprints.nottingham.ac.uk/53022/ https://eprints.nottingham.ac.uk/53022/ https://eprints.nottingham.ac.uk/53022/ |